Bibliographic record
Abstract
In our previous editorial we discussed two significant interrelated exigencies in the field of English for Research Publication Purposes (ERPP): the role of technology in the dynamics and developments of the processes and practices of knowledge construction and dissemination, and the pedagogy of ERPP as an under-researched and under-represented domain.An issue that is attracting increasing attention in 2023 is the key role that Artificial Intelligence (AI) can play / is playing in changing the landscape and dynamics of scholarly work, including academic publication.The appearance of technologies such as ChatGPT as an open AI technology in late 2022 is a good example in that respect.The emergence of such technologies raises this important question: Is AI the new normalcy in our academic life and will it revolutionize the way we interact, create, and circulate knowledge?Certainly, we are facing issues regarding the philosophy, integrity, and ethics of knowledge production and dissemination and new imaginations in ERPP in particular.As the growing discussions both online and in-person show, both academia and the general public are marvelled by the affordances and capabilities of emerging AI technologies such as ChatGPT, Google's Bard and Microsoft's Sydney.However, what is, still controversial and debatable is the capacities of such technologies for producing human-like discourse, thought, and learning and how, and to what extent, such technologies can impact the dynamics of knowledge production and exchange.Some scholars such as Noam Chomsky prefer to be on the cautious side and are hesitant as to whether mechanical minds can be on a par with or improve on human brains.Although Chomsky and colleagues consider such technologies as a step forward, they warn against their "false promise" claiming that ChatGPT "exhibits something like the banality of evil: plagiarism and apathy and obviation" (Chomsky et al., 2023, para.17).They argue that:
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.105 | 0.039 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".